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Head-to-Head Comparison

DryRun Security vs Corridor

Agentic Coding Security Management — Pre-generation guardrails securing code at the point of AI generation

Compare with: Snyk Code Snyk Evo GHAS Claude Code Codex Veracode ZeroPath DepthFirst Corgea Aikido Semgrep Sonar Corridor OX Security Qwiet AI
21
DryRun Leads
12
Tie
1
Corridor Leads
34
Capabilities Compared
Capability DryRun Security Corridor Verdict
AI & Intelligence7
AI-Native Architecture
AI-native since 2023; model-independent; multi-agent agentic system (Code Review Agent, DeepScan Agent, Custom Policy Agent, Codebase Insight Agent)
~
Built for AI-era security but primarily a guardrails layer, not a full AI-native security analysis platform
DryRun leads
Business Logic Flaw Detection
IDOR, broken auth, multi-tenant isolation, logic flaws, mass assignment, privilege escalation, TOCTOU race conditions, OAuth failures, WebSocket auth bypass; 88% detection OOTB; outperformed 5 leading SAST tools (2025 SAST Accuracy Report)
~
Reviews PR-level logic but not deep SAST analysis
DryRun leads
Contextual / Semantic Code Analysis
Contextual Security Analysis (CSA): data flow, architecture, change history, intent, exploitability; detects issues pattern-based SAST cannot — middleware defined but not mounted, trust boundary misalignment, config not wired up; reads AGENTS.md
Understands AI coding context and development patterns; codebase learning for guardrails
Tie
Vulnerability Coverage Breadth
48+ vulnerability categories: SQLi, XSS, SSRF, IDOR, RCE, auth bypass, CSRF, XXE, path traversal, prompt injection, LLM tool misuse, OAuth failures, TOCTOU, WebSocket auth bypass, and more
guardrails layer; focuses on AI-generated code security, not traditional vuln scanning
DryRun leads
Git Behavioral Analysis
Git Behavioral Graphs: code churn, temporal coupling, knowledge decay, temporal anomalies, intent mining
DryRun leads
Natural Language Policies
Natural Language Code Policies (NLCP); Policy Library with 16+ pre-built policies; Custom Policy Agent enforces on every PR
Define security policies in plain English; core product mechanism
Tie
False Positive Reduction
90% lower noise; CSA-driven reasoning; Risk Register dismissal with fingerprinting suppresses FPs in future scans
~
Focused on high-signal, actionable findings from AI-generated code
DryRun leads
AI Coding Agent Security6
Securing AI-Generated Code
Reviews all code equally — human or AI-generated; model-independent verification layer; Agentic Coding Security Report (Mar 2026): 143 issues found across Claude/Codex/Gemini builds, 87% of PRs had vulns
Core and entire purpose of product: real-time guardrails at point of AI code generation
Tie
Malicious AI Agent Skill Detection
Policy Library includes Malicious AI Agent Skills Detection: flags skills/plugins that could enable data theft, backdoors, or code execution
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Monitors AI agent behavior for policy violations; guardrails may catch malicious patterns
DryRun leads
MCP Integration
DryRun Insights MCP server: security summaries, PR analysis, trend monitoring, file-level history; connects via Direct HTTP, Claude Shortcuts, or mcp-remote
MCP server; core product mechanism for real-time agent context
Tie
AI Coding Tool Integrations
Native integrations: Cursor, Codex, Claude Code, Windsurf, VS Code (via Insights MCP + Add Skill); reviews output of any AI tool via PR workflow
Cursor, Factory, Claude Code, Copilot (hooks + MCP)
Tie
AI Coding Visibility / Observability
Code Insights with AI Assistance (beta): NL queries for risk, trends, exposure; org-wide visibility; per-repo drill-down; file-level security history
Flagship capability: observability dashboard for all AI coding activity; org-wide AI usage tracking
Tie
AI Red Teaming / Threat Modeling Tie
Code Security Intelligence3
Code Security Knowledge Graph
Accumulates organizational knowledge across PRs; cross-repo intelligence; learns risk tolerance from dismissal patterns (nitpicks, FPs, accepted risks); FP fingerprinting improves decision quality over time
DryRun leads
Model-Independent Verification
Separates code generation from code verification; works regardless of which AI model or human generates code
DryRun leads
Continuous Baseline & Risk Trending
Risk Register with Critical/High/Medium/Low severity; AI Assistance for Insights with NL queries, trend monitoring, and 30-day window analysis
DryRun leads
Core Detection6
SAST (Static Analysis)
AI-native Contextual Security Analysis engine; agentic multi-agent architecture; works on human and AI-generated code alike
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PR reviews with security guardrails. No dedicated SAST engine; relies on analyzing AI agent output.
DryRun leads
DAST (Dynamic Analysis) Tie
SCA (Dependency / Supply Chain)
SCA agent with dependency and supply chain analysis; Risk Register tracks SCA findings by severity
DryRun leads
Secrets Detection
AI-native secrets analyzer; detects obfuscated secrets (concatenation, base64, logging); hard-coded credentials policy in Policy Library
~
Can detect secrets in AI-generated code but not a standalone secrets tool
DryRun leads
IaC Scanning
IaC scanning (Terraform, YAML, and infrastructure-as-code analysis)
DryRun leads
Container Scanning Tie
Remediation & Fixes3
Auto-Fix / AI Remediation
Tessl remediation skill for AI coding tools: extracts finding, researches authoritative sources, applies context-grounded fixes in the developer's codebase; co-authored commits; works in Cursor, Claude Code, Codex, VS Code
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Prevention model: blocks insecure code before commit. Not traditional post-scan remediation.
DryRun leads
Fix Verification / Re-testing
Re-runs DryRun Security analysis after remediation is applied to verify the fix resolves the finding
Prevention-only model.
DryRun leads
Finding Dismissal & Triage Workflow
Risk Register with structured dismissal: Accepted Risk, False Positive, In Progress, Resolved, Won't Fix / Nitpick; learns risk tolerance of the repo and org from dismissal patterns (nitpicks, FPs, accepted risks); developer dismissal from PR comments (GitHub + GitLab)
DryRun leads
Developer Workflow5
PR / Merge Request Reviews
Every PR; real-time contextual feedback; pass/fail checks; inline explanations; reads AGENTS.md for project context
Security review on AI-generated PRs; enforces policies pre-merge
Tie
Full Repository / Deep Scan
DeepScan Agent: full-repo security review in hours; discovers root and nested AGENTS.md for context; findings flow to Risk Register
DryRun leads
IDE Integration
DryRun Insights MCP integrates with VS Code, Cursor, Windsurf, Claude Code, and Codex for security-aware coding assistance
VS Code, Cursor, Claude Code CLI
Tie
CI/CD Integration
GitHub and GitLab native integration; webhook notifications (Slack + generic)
DryRun leads
SCM Support GitHub and GitLab (native apps with OAuth) GitHub only currently; scaling to GitLab/Bitbucket pending Tie
Coverage2
Language Support
15+ languages optimized: Python, JS/TS, Ruby, Go, C#, Java, Kotlin, PHP, Swift, Elixir, HTML, IaC (Terraform, YAML)
guardrails layer, not a scanner
DryRun leads
Out-of-Box Accuracy (No Tuning)
88% detection rate OOTB; 2x more accurate than nearest competitor in independent testing
DryRun leads
Reporting & Compliance3
Security Dashboard / Analytics
Risk Register (Critical/High/Medium/Low); AI Assistance for Insights with NL queries; Codebase Insight Agent; per-repo and file-level drill-down
Org-wide visibility dashboard for all AI coding activity
Tie
Compliance / Audit Readiness ~
Audit-ready reporting; policy enforcement evidence; structured finding dismissals with reasons and context
Complete audit logs for all AI coding activity; compliance-ready for AI governance
Competitor leads
SBOM / AI-BOM Generation
DeepScan generates SBOM; SCA agent provides dependency inventory and license checking (Dependency License Check policy)
DryRun leads
Architecture & Positioning4
Agentic / Multi-Agent System
Code Review Agent, Custom Policy Agent, DeepScan Agent, Codebase Insight Agent + specialized sub-agents; AGENTS.md support (Linux Foundation)
~
Background security layer (hooks + MCP); not full multi-agent orchestration
DryRun leads
API / Extensibility
DryRun Simple API (REST); Swagger/OpenAPI spec; webhook integrations (Slack + generic); MCP server
DryRun leads
Approach / Category
Code Security Intelligence: continuous, model-independent layer that understands, evaluates, and enforces code security for both human and AI-generated code; used to benchmark Claude, Codex, and Gemini security (Agentic Coding Security Report, Mar 2026)
Agentic Coding Security Management (ACSM): pre-generation guardrails for AI-generated code ($30.4M raised; founded by Jack Cable ex-CISA)
Key Structural Differentiator
Durable knowledge graph + model-independent verification: accumulates proprietary data about code behavior, vuln patterns, and org risk posture; proven benchmarking tool for AI coding agent security (Agentic Coding Security Report, Mar 2026)
Only product focused on securing code at the point of AI generation (pre-commit); founded by Jack Cable (CISA/DHS); most developed AI Coding Visibility in market
Market Feedback (G2)4
G2 Rating / Review Count
4.9/5 (19 reviews) — g2.com/products/dryrun-security/reviews
No G2 reviews
Notable G2 Praise (Attributed)
"DryRun goes far beyond what rule-based SAST tools offer. It catches things other tools completely miss — like middleware that's defined but never mounted, or trust boundary misalignments." — Jabez A., Director, Product Security Architecture, Enterprise (g2.com/products/dryrun-security/reviews)
No G2 reviews available
Notable G2 Criticisms (Attributed)
"I do somewhat wish there were more customization options for tuning the analyzers, but that seems to be in the works." — Kyle R. (g2.com/products/dryrun-security/reviews)
No G2 reviews available
Common G2 Complaint Themes
UI/portal speed; desire for more analyzer customization (g2.com/products/dryrun-security/reviews)
Too new for G2 feedback

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